Publication detail

Deep Generative Networks for Algorithm Development in Implantable Neural Technology

MÍVALT, F. SLADKÝ, V. BALZEKAS, I. PŘIDALOVÁ, T. MILLER, K. VAN GOMPEL, J. DENISON, T. BRINKMANN, B. KŘEMEN, V. WORRELL, G.

Original Title

Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Type

conference paper

Language

English

Original Abstract

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications.

Keywords

DBS, DCGAN, synthetic iEEG, DBS artifact removal, implantable neural technology

Authors

MÍVALT, F.; SLADKÝ, V.; BALZEKAS, I.; PŘIDALOVÁ, T.; MILLER, K.; VAN GOMPEL, J.; DENISON, T.; BRINKMANN, B.; KŘEMEN, V.; WORRELL, G.

Released

9. 10. 2022

Publisher

IEEE

Location

Prague. Czechia

ISBN

978-1-6654-5258-8

Book

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Pages from

1736

Pages to

1741

Pages count

6

URL

https://ieeexplore.ieee.org/document/9945379

BibTex

@inproceedings{BUT180108,
  author="Filip {Mívalt} and Vladimír {Sladký} and Irena {Balzekas} and Tereza {Přidalová} and Kai J. {Miller} and Jamie J. {Van Gompel} and Timothy {Denison} and Benjamin H. {Brinkmann} and Václav {Křemen} and Gregory {Worrell}",
  title="Deep Generative Networks for Algorithm Development in Implantable Neural Technology",
  booktitle="2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
  year="2022",
  pages="1736--1741",
  publisher="IEEE",
  address="Prague. Czechia",
  doi="10.1109/SMC53654.2022.9945379",
  isbn="978-1-6654-5258-8",
  url="https://ieeexplore.ieee.org/document/9945379"
}